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SUMMARY:Methodological advances in creating time sensitive sensors from la
 nguage and heterogeneous user generated content - Maria Liakata (Queen Mar
 y University of London\, University of Warwick\, Alan Turing Institute)
DTSTART:20200529T110000Z
DTEND:20200529T120000Z
UID:TALK142255@talks.cam.ac.uk
CONTACT:Guy Aglionby
DESCRIPTION:A large body of work in natural language processing (NLP) for 
 clinical applications is based on processing electronic health records (EH
 Rs). While the latter are rich in information there are typically only few
  records per patient. More recently there has been interest in processing 
 user generated content (UGC) such as social media posts collected over tim
 e to make predictions about individuals' mental health. Such UGC data is a
 vailable at much more frequent temporal intervals than EHRs but may be noi
 sier. So far the majority of work in NLP on mental health prediction\, eve
 n when using longitudinal social media data\, involves distinguishing indi
 viduals with a condition from controls rather than assessing individuals
 ’ mental health at different points in time. This talk will present the 
 objectives addressed by my five-year Turing AI Fellowship through which I 
 aim to establish a new area in natural language processing on personalised
  longitudinal language processing. I will give an overview of the state-of
 -the-art in this area\, the challenges involved and work in progress on de
 veloping sensors for capturing digital biomarkers from language and hetero
 geneous UGC to understand the evolution of an individual over time.\n\n\nB
 io: Maria Liakata is a Turing AI fellow and Professor in Natural Language 
 Processing (NLP) at the School of Electronic Engineering and Computer Scie
 nce\, Queen Mary University of London and the Department of Computer Scien
 ce\, University of Warwick. At the Turing she founded and co-leads the NLP
  and data science for mental health interest groups and supervises PhD stu
 dents. She is in receipt of one of the five Turing AI fellowships\, on Cre
 ating time sensitive sensors from user-generated language and heterogeneou
 s content. She is the PI of projects on “Emotion sensing using heterogen
 eous mobile phone data”\, “Language sensing for dementia monitoring & 
 diagnosis” and “Opinion summarisation from social media”. Her resear
 ch interests include opinion mining and summarisation\, NLP for social and
  biomedical applications\, longitudinal models of multi-modal and heteroge
 neous data\, rumour verification.
LOCATION:https://meet.google.com/awc-wvbh-azc
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